Parallel Machine Learning Using Concurrency Control

Xinghao Pan

Many machine learning algorithms iteratively process datapoints and transform global model parameters. It has become increasingly impractical to serially execute such iterative algorithms as processor speeds fail to catch up to the growth in dataset sizes. To address these problems, the machine learning community has turned to two parallelization strategies: bulk synchronous parallel (BSP), and coordination-free. BSP algorithms partition computational work among workers, with occasional synchronization at global barriers, but has only been applied to ‘embarrassingly parallel’ problems where work is trivially factorizable. Coordination-free algorithms simply allow concurrent processors to execute in parallel, interleaving transformations and possibly introducing inconsistencies. Theoretical analysis is then required to prove that the coordination-free algorithm produces a reasonable approximation to the desired outcome, under assumptions on the problem and system. In this dissertation, we propose and explore a third approach by applying concurrency control to manage parallel transformations in machine learning algorithms. We identify points of possible interference between parallel iterations by examining the semantics of the serial algorithm. Coordination is then introduced to either avoid or resolve such conflicts, whereas non-conflicting transformations are allowed to execute concurrently. Our parallel algorithms are thus engineered to produce the same exact output as the serial machine learning algorithm, preserving the serial algorithm’s theoretical guarantees of correctness while maximizing concurrency. We demonstrate the feasibility of our approach to parallelizing a variety of machine learning algorithms, including nonparametric unsupervised learning, graph clustering, discrete optimization, and sparse convex optimization. We theoretically prove and empirically verify that our parallel algorithms produce equivalent output to their serial counterparts. We also theoretically analyze the expected concurrency of our parallel algorithms, and empirically demonstrate their scalability.

Advisor: Michael Jordan

BibTeX citation:

@phdthesis{Pan:EECS-2017-138,
Author = {Pan, Xinghao},
Title = {Parallel Machine Learning Using Concurrency Control},
School = {EECS Department, University of California, Berkeley},
Year = {2017},
Month = {Aug},
URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-138.html},
Number = {UCB/EECS-2017-138},
Abstract = {Many machine learning algorithms iteratively process datapoints and transform global model parameters. It has become increasingly impractical to serially execute such iterative algorithms as processor speeds fail to catch up to the growth in dataset sizes.
To address these problems, the machine learning community has turned to two parallelization strategies: bulk synchronous parallel (BSP), and coordination-free. BSP algorithms partition computational work among workers, with occasional synchronization at global barriers, but has only been applied to ‘embarrassingly parallel’ problems where work is trivially factorizable. Coordination-free algorithms simply allow concurrent processors to execute in parallel, interleaving transformations and possibly introducing inconsistencies. Theoretical analysis is then required to prove that the coordination-free algorithm produces a reasonable approximation to the desired outcome, under assumptions on the problem and system.
In this dissertation, we propose and explore a third approach by applying concurrency control to manage parallel transformations in machine learning algorithms. We identify points of possible interference between parallel iterations by examining the semantics of the serial algorithm. Coordination is then introduced to either avoid or resolve such conflicts, whereas non-conflicting transformations are allowed to execute concurrently. Our parallel algorithms are thus engineered to produce the same exact output as the serial machine learning algorithm, preserving the serial algorithm’s theoretical guarantees of correctness while maximizing concurrency.
We demonstrate the feasibility of our approach to parallelizing a variety of machine learning algorithms, including nonparametric unsupervised learning, graph clustering, discrete optimization, and sparse convex optimization. We theoretically prove and empirically verify that our parallel algorithms produce equivalent output to their serial counterparts. We also theoretically analyze the expected concurrency of our parallel algorithms, and empirically demonstrate their scalability.}
}